Building Spatial Databases with Attributes

HES 505 Fall 2022: Session 14

Matt Williamson

Today’s Plan

Objectives

  • By the end of today, you should be able to:

    • Define spatial analysis

    • Describe the steps in planning a spatial analysis

    • Understand the structure of relational databases

    • Begin building a database for spatial analysis

What is spatial analysis?

What is spatial analysis?

“The process of examining the locations, attributes, and relationships of features in spatial data through overlay and other analytical techniques in order to address a question or gain useful knowledge. Spatial analysis extracts or creates new information from spatial data”.
— ESRI Dictionary

What is spatial analysis?

  • The process of turning maps into information

  • Any- or everything we do with GIS

  • The use of computational and statistical algorithms to understand the relations between things that co-occur in space.

John Snow’s cholera outbreak map

Common goals for spatial analysis

courtesy of NatureServe

  • Describe and visualize locations or events

  • Quantify patterns

  • Characterize ‘suitability’

  • Determine (statistical) relations

Common pitfalls of spatial analysis

  • Locational Fallacy: Error due to the spatial characterization chosen for elements of study

  • Atomic Fallacy: Applying conclusions from individuals to entire spatial units

  • Ecological Fallacy: Applying conclusions from aggregated information to individuals

Spatial analysis is an inherently complex endeavor and one that is advancing rapidly. So-called “best practices” for addressing many of these issues are still being developed and debated. This doesn’t mean you shouldn’t do spatial analysis, but you should keep these things in mind as you design, implement, and interpret your analyses

Workflows for spatial analysis

Workflows for spatial analysis

  • Acquisition (not really a focus, but see Resources)

  • Geoprocessing

  • Analysis

  • Visualization

courtesy of University of Illinois

Geoprocessing

Manipulation of data for subsequent use

  • Alignment (projections, cropping)

  • Data cleaning and transformation (measures, transformers)

  • Combination of multiple datasets (overlays, raster maths)

  • Selection and subsetting (predicates, measures)